HFMD Skin Rash Detection Using Convolutional Neural Networks
Conference proceedings article
ผู้เขียน/บรรณาธิการ
กลุ่มสาขาการวิจัยเชิงกลยุทธ์
รายละเอียดสำหรับงานพิมพ์
รายชื่อผู้แต่ง: Vakili, Naqibullah; Phattarakijtham, Nipat; Chan, Jonathan H.; Krathu, Worarat;
ผู้เผยแพร่: Springer Science and Business Media Deutschland GmbH
ปีที่เผยแพร่ (ค.ศ.): 2021
ชื่อชุด: Lecture Notes in Network and Systems (LNNS)
Volume number: 251
หน้าแรก: 159
หน้าสุดท้าย: 168
จำนวนหน้า: 10
ISBN: 9783030797560
นอก: 23673370
ภาษา: English-Great Britain (EN-GB)
บทคัดย่อ
Skin rash problems are common and often temporary if they can be treated in time. Hand-Foot-Mouth disease (HFMD) is an extremely contagious viral infection common in infants, and can quickly develop into a severe problem. The infection can spread quickly through close contact with an infected person. HFMD usually involves the hands, feet and mouth, but sometimes can be detected elsewhere. To assist in diagnosing HFMD, we built a dataset of various skin conditions from readily available web sources. We propose the use of a deep convolutional neural networks (CNN) to distinguish HFMD rash from other skin conditions and normal skin. This study presents a promising application of CNN with a precision rate of 0.92%, 0.96%, and 0.88 for HFMD, non-HFMD, and normal skin, respectively. Our proposed model can facilitate a proper and early detection of HFMD rash to assist in containing HFMD outbreaks regionally. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
คำสำคัญ
Convolutional neural networks (CNN), Deep Learning, Hand, Foot and Mouth Disease, HFMD, Multiclass classification
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